Overview

Dataset statistics

Number of variables14
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory347.9 KiB
Average record size in memory120.0 B

Variable types

Numeric14

Alerts

avg_basket_size is highly overall correlated with avg_ticket and 3 other fieldsHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
avg_ticket is highly overall correlated with avg_basket_size and 3 other fieldsHigh correlation
delta_buy_return is highly overall correlated with avg_basket_size and 5 other fieldsHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
gross_revenue is highly overall correlated with avg_basket_size and 5 other fieldsHigh correlation
items_purchased is highly overall correlated with avg_basket_size and 5 other fieldsHigh correlation
monetary_returns is highly overall correlated with number_returnsHigh correlation
number_returns is highly overall correlated with monetary_returnsHigh correlation
purchases is highly overall correlated with delta_buy_return and 4 other fieldsHigh correlation
recency_days is highly overall correlated with purchasesHigh correlation
unique_avg_basket is highly overall correlated with unique_products_purchasedHigh correlation
unique_products_purchased is highly overall correlated with delta_buy_return and 4 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 47.52624678)Skewed
frequency is highly skewed (γ1 = 24.88049136)Skewed
monetary_returns is highly skewed (γ1 = 51.677877)Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
number_returns has 1481 (49.9%) zerosZeros
monetary_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2024-01-26 13:04:09.688760
Analysis finished2024-01-26 13:04:50.694301
Duration41.01 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:51.124861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2024-01-26T10:04:51.338870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.3217
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:51.553001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.623
Coefficient of variation (CV)3.8484486
Kurtosis353.94472
Mean2749.3217
Median Absolute Deviation (MAD)672.16
Skewness16.777556
Sum8162736.2
Variance1.1194959 × 108
MonotonicityNot monotonic
2024-01-26T10:04:51.774010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.287639
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:52.003888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756779
Coefficient of variation (CV)1.2095137
Kurtosis2.7779627
Mean64.287639
Median Absolute Deviation (MAD)26
Skewness1.7983795
Sum190870
Variance6046.1167
MonotonicityNot monotonic
2024-01-26T10:04:52.240298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

purchases
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7231391
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:52.473032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8565313
Coefficient of variation (CV)1.5474954
Kurtosis190.83445
Mean5.7231391
Median Absolute Deviation (MAD)2
Skewness10.766805
Sum16992
Variance78.438147
MonotonicityNot monotonic
2024-01-26T10:04:52.697135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

unique_products_purchased
Real number (ℝ)

HIGH CORRELATION 

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.72415
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:52.907631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.89641
Coefficient of variation (CV)2.1992119
Kurtosis354.86113
Mean122.72415
Median Absolute Deviation (MAD)44
Skewness15.707635
Sum364368
Variance72844.071
MonotonicityNot monotonic
2024-01-26T10:04:53.144176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
35 35
 
1.2%
29 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2629
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

items_purchased
Real number (ℝ)

HIGH CORRELATION 

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.8525
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:53.389957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median641
Q31401
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1105

Descriptive statistics

Standard deviation5887.578
Coefficient of variation (CV)3.6594891
Kurtosis465.99808
Mean1608.8525
Median Absolute Deviation (MAD)422
Skewness17.858591
Sum4776683
Variance34663575
MonotonicityNot monotonic
2024-01-26T10:04:53.636434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 9
 
0.3%
88 9
 
0.3%
246 8
 
0.3%
272 8
 
0.3%
84 8
 
0.3%
260 8
 
0.3%
288 8
 
0.3%
1200 7
 
0.2%
516 7
 
0.2%
Other values (1661) 2886
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2957
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.40904
Minimum6.2
Maximum84236.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:53.890139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile106.69186
Q1195.26333
median305.593
Q3442.735
95-th percentile921.62714
Maximum84236.25
Range84230.05
Interquartile range (IQR)247.47167

Descriptive statistics

Standard deviation1614.6022
Coefficient of variation (CV)3.8314371
Kurtosis2451.1323
Mean421.40904
Median Absolute Deviation (MAD)117.273
Skewness47.526247
Sum1251163.4
Variance2606940.3
MonotonicityNot monotonic
2024-01-26T10:04:54.201872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162.6075 2
 
0.1%
189.825 2
 
0.1%
230.74 2
 
0.1%
394.5 2
 
0.1%
145.525 2
 
0.1%
186.265 2
 
0.1%
512.72 2
 
0.1%
199.4 2
 
0.1%
200.95 2
 
0.1%
211.32 2
 
0.1%
Other values (2947) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
9.14 1
< 0.1%
11.67 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
20.81 1
< 0.1%
26 1
< 0.1%
26.1 1
< 0.1%
28.21666667 1
< 0.1%
28.73142857 1
< 0.1%
ValueCountFrequency (%)
84236.25 1
< 0.1%
14844.76667 1
< 0.1%
9341.26 1
< 0.1%
6228.2265 1
< 0.1%
4327.621667 1
< 0.1%
4279.71 1
< 0.1%
4229.365 1
< 0.1%
3914.945 1
< 0.1%
3883.985385 1
< 0.1%
3876.916944 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION 

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.348511
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:54.499279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.923077
median48.285714
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.410256

Descriptive statistics

Standard deviation63.544929
Coefficient of variation (CV)0.94352388
Kurtosis4.8871091
Mean67.348511
Median Absolute Deviation (MAD)26.285714
Skewness2.0627709
Sum199957.73
Variance4037.958
MonotonicityNot monotonic
2024-01-26T10:04:54.727564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
11 17
 
0.6%
42 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137973
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:54.962317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049450549
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.03311068

Descriptive statistics

Standard deviation0.40815625
Coefficient of variation (CV)3.5866953
Kurtosis989.36508
Mean0.1137973
Median Absolute Deviation (MAD)0.012191338
Skewness24.880491
Sum337.8642
Variance0.16659153
MonotonicityNot monotonic
2024-01-26T10:04:55.518438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2636
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

number_returns
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.122937
Minimum0
Maximum45
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:55.757945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2970249
Coefficient of variation (CV)2.045551
Kurtosis114.47695
Mean1.122937
Median Absolute Deviation (MAD)1
Skewness8.0245935
Sum3334
Variance5.2763234
MonotonicityNot monotonic
2024-01-26T10:04:55.943327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1481
49.9%
1 833
28.1%
2 289
 
9.7%
3 140
 
4.7%
4 92
 
3.1%
5 37
 
1.2%
6 32
 
1.1%
7 21
 
0.7%
9 8
 
0.3%
11 5
 
0.2%
Other values (13) 31
 
1.0%
ValueCountFrequency (%)
0 1481
49.9%
1 833
28.1%
2 289
 
9.7%
3 140
 
4.7%
4 92
 
3.1%
5 37
 
1.2%
6 32
 
1.1%
7 21
 
0.7%
8 5
 
0.2%
9 8
 
0.3%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
35 1
 
< 0.1%
27 1
 
< 0.1%
21 1
 
< 0.1%
18 2
 
0.1%
17 1
 
< 0.1%
15 2
 
0.1%
14 1
 
< 0.1%
13 5
0.2%

monetary_returns
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1077
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.42163
Minimum0
Maximum168469.6
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:56.217903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.95
Q330
95-th percentile230.742
Maximum168469.6
Range168469.6
Interquartile range (IQR)30

Descriptive statistics

Standard deviation3149.1565
Coefficient of variation (CV)23.781285
Kurtosis2754.6213
Mean132.42163
Median Absolute Deviation (MAD)0.95
Skewness51.677877
Sum393159.82
Variance9917186.6
MonotonicityNot monotonic
2024-01-26T10:04:56.492272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
4.95 19
 
0.6%
12.75 17
 
0.6%
9.95 17
 
0.6%
15 16
 
0.5%
5.9 12
 
0.4%
4.25 10
 
0.3%
25.5 10
 
0.3%
3.75 9
 
0.3%
17 8
 
0.3%
Other values (1067) 1370
46.1%
ValueCountFrequency (%)
0 1481
49.9%
0.42 2
 
0.1%
0.65 1
 
< 0.1%
0.95 1
 
< 0.1%
1.25 4
 
0.1%
1.45 4
 
0.1%
1.64 1
 
< 0.1%
1.65 5
 
0.2%
1.7 2
 
0.1%
1.79 1
 
< 0.1%
ValueCountFrequency (%)
168469.6 1
< 0.1%
22998.4 1
< 0.1%
14688.24 1
< 0.1%
8511.15 1
< 0.1%
7443.59 1
< 0.1%
5228.4 1
< 0.1%
4815.26 1
< 0.1%
4814.74 1
< 0.1%
4486.24 1
< 0.1%
4429 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.81376
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:57.116145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.33333
Q3281.69231
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.44231

Descriptive statistics

Standard deviation791.55519
Coefficient of variation (CV)3.1685812
Kurtosis2255.5382
Mean249.81376
Median Absolute Deviation (MAD)83.083333
Skewness44.672717
Sum741697.07
Variance626559.62
MonotonicityNot monotonic
2024-01-26T10:04:57.649461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
136 8
 
0.3%
208 7
 
0.2%
Other values (1969) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

unique_avg_basket
Real number (ℝ)

HIGH CORRELATION 

Distinct1005
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.154708
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-01-26T10:04:57.967714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.512322
Coefficient of variation (CV)0.88073027
Kurtosis27.703297
Mean22.154708
Median Absolute Deviation (MAD)8.2
Skewness3.4994559
Sum65777.329
Variance380.73071
MonotonicityNot monotonic
2024-01-26T10:04:58.316923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 39
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 32
 
1.1%
17 31
 
1.0%
18 30
 
1.0%
10 30
 
1.0%
5 29
 
1.0%
Other values (995) 2621
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

delta_buy_return
Real number (ℝ)

HIGH CORRELATION 

Distinct2952
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2616.9001
Minimum-796.86
Maximum278778.02
Zeros8
Zeros (%)0.3%
Negative3
Negative (%)0.1%
Memory size46.4 KiB
2024-01-26T10:04:58.585180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-796.86
5-th percentile213
Q1551.54
median1054.73
Q32265.12
95-th percentile7017.35
Maximum278778.02
Range279574.88
Interquartile range (IQR)1713.58

Descriptive statistics

Standard deviation9927.6384
Coefficient of variation (CV)3.7936635
Kurtosis422.03959
Mean2616.9001
Median Absolute Deviation (MAD)656.03
Skewness18.204198
Sum7769576.3
Variance98558004
MonotonicityNot monotonic
2024-01-26T10:04:58.876423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.3%
178.96 2
 
0.1%
1078.96 2
 
0.1%
331 2
 
0.1%
599.9 2
 
0.1%
306.55 2
 
0.1%
379.65 2
 
0.1%
589.15 2
 
0.1%
598.2 2
 
0.1%
582.9 2
 
0.1%
Other values (2942) 2943
99.1%
ValueCountFrequency (%)
-796.86 1
 
< 0.1%
-141.48 1
 
< 0.1%
-95.93 1
 
< 0.1%
0 8
0.3%
5.684341886 × 10-141
 
< 0.1%
4.547473509 × 10-131
 
< 0.1%
2.9 1
 
< 0.1%
12.24 1
 
< 0.1%
15 1
 
< 0.1%
36.56 1
 
< 0.1%
ValueCountFrequency (%)
278778.02 1
< 0.1%
259657.3 1
< 0.1%
189735.53 1
< 0.1%
133007.13 1
< 0.1%
123638.18 1
< 0.1%
114505.32 1
< 0.1%
88138.2 1
< 0.1%
65920.12 1
< 0.1%
62924.1 1
< 0.1%
59419.34 1
< 0.1%

Interactions

2024-01-26T10:04:47.241316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:12.078551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:15.060147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:17.699932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:20.308752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:22.684050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:25.342935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:28.016471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:30.627223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:33.208634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:36.173544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:38.757765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:41.421150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:44.576343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:47.399645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:12.512171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:15.227569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:17.899567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:20.469522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:22.857306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:25.522049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:28.476335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:30.800650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:33.422458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:36.370153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:38.942883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:41.617611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:44.755564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:47.553671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:12.702367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:15.397987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:18.095384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:20.639406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:23.035927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:25.689584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:28.635360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:30.974969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:33.657472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:36.561829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:39.142573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:41.850039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:44.938977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:47.749527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:12.872264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:15.632177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:18.287790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:20.818790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:23.259927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:25.883152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:28.798710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:31.154379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:33.890840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:36.809675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:39.318033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:42.025266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:45.139069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:47.899637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:13.023788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:15.797431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:18.449833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:20.968029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:23.457238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:26.052590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:28.950680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:31.319048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:34.061604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:37.065778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:39.476401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:42.188891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:45.383666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:48.083244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:13.207756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:15.993390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:18.640428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:21.141911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:23.650805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:26.250156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:29.128590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:31.511333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:34.266398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:37.287959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:39.688885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:42.380642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:45.610227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:48.261084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:13.392355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:16.182393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:18.840837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:21.321699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:23.853181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:26.458306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:29.303760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:31.713553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:34.471249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:37.464763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:39.962348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:42.582183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:45.791124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:48.421076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:13.549673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:16.360807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:19.013919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:21.464643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:24.016105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:26.631505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:29.450647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:31.890257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:34.657475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:37.603393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:40.130809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:42.748794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:45.945735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:48.597637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:13.728976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:16.559309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:19.197899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:21.631948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:24.220235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:26.817333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:29.615189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:32.078349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:34.870010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:37.761521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:40.325841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:42.927161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:46.116529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:48.775462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:13.921573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:16.737070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:19.383171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:21.803899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:24.418782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:27.001527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:29.786551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:32.271932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:35.049054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:37.930145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:40.503703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:43.178499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:46.288959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:48.925231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:14.278139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:16.920750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:19.542991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:21.963979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:24.589998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:27.166242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:29.932478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:32.455436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:35.266059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:38.075373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:40.668106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:43.818301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:46.453927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:49.091831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:14.460491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:17.121309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:19.738633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:22.161439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:24.781254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:27.411061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:30.110623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:32.658045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:35.564776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:38.243053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:40.856300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:44.008937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:46.720666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:49.268114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:14.662010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:17.337555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:19.936485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:22.357524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:24.978560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:27.615797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:30.293499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:32.858434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:35.782330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:38.421622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:41.042086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:44.207490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:46.903512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:49.429784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:14.881103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:17.518548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:20.123498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:22.523344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:25.159694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:27.792994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:30.455884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:33.030015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:35.976746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:38.607299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:41.231414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:44.396027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T10:04:47.070607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-01-26T10:04:59.126240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
avg_basket_sizeavg_recency_daysavg_ticketcustomer_iddelta_buy_returnfrequencygross_revenueitems_purchasedmonetary_returnsnumber_returnspurchasesrecency_daysunique_avg_basketunique_products_purchased
avg_basket_size1.000-0.0770.824-0.1230.5680.0270.5740.7290.1790.1780.100-0.0980.4470.383
avg_recency_days-0.0771.000-0.1130.019-0.238-0.881-0.247-0.227-0.397-0.421-0.2590.1080.048-0.166
avg_ticket0.824-0.1131.000-0.1410.6630.0560.6740.6220.2580.2440.107-0.0730.4250.374
customer_id-0.1230.019-0.1411.000-0.072-0.002-0.076-0.070-0.056-0.0490.0260.001-0.0070.013
delta_buy_return0.568-0.2380.663-0.0721.0000.0830.9920.9210.3290.3630.774-0.4180.2990.752
frequency0.027-0.8810.056-0.0020.0831.0000.0900.0800.2390.2360.0790.018-0.0720.036
gross_revenue0.574-0.2470.674-0.0760.9920.0901.0000.9250.3720.3910.770-0.4150.2910.744
items_purchased0.729-0.2270.622-0.0700.9210.0800.9251.0000.3260.3500.716-0.4080.3200.730
monetary_returns0.179-0.3970.258-0.0560.3290.2390.3720.3261.0000.9430.295-0.1190.0170.242
number_returns0.178-0.4210.244-0.0490.3630.2360.3910.3500.9431.0000.338-0.1430.0310.279
purchases0.100-0.2590.1070.0260.7740.0790.7700.7160.2950.3381.000-0.5020.0250.690
recency_days-0.0980.108-0.0730.001-0.4180.018-0.415-0.408-0.119-0.143-0.5021.000-0.106-0.435
unique_avg_basket0.4470.0480.425-0.0070.299-0.0720.2910.3200.0170.0310.025-0.1061.0000.699
unique_products_purchased0.383-0.1660.3740.0130.7520.0360.7440.7300.2420.2790.690-0.4350.6991.000

Missing values

2024-01-26T10:04:49.756008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-26T10:04:50.136205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_dayspurchasesunique_products_purchaseditems_purchasedavg_ticketavg_recency_daysfrequencynumber_returnsmonetary_returnsavg_basket_sizeunique_avg_basketdelta_buy_return
0178505391.21372.034.0297.01733.0158.56500035.50000017.0000001.0102.5850.9705888.7352945288.63
1130473232.5956.09.0171.01390.0359.17666727.2500000.0283027.0143.49154.44444419.0000003089.10
2125836705.382.015.0232.05028.0447.02533323.1875000.0403232.076.04335.20000015.4666676629.34
313748948.2595.05.028.0439.0189.65000092.6666670.0179210.00.0087.8000005.600000948.25
415100876.00333.03.03.080.0292.0000008.6000000.0731713.0240.9026.6666671.000000635.10
5152914623.3025.014.0102.02102.0330.23571423.2000000.0401155.071.79150.1428577.2857144551.51
6146885630.877.021.0327.03621.0268.13666718.3000000.0572216.0523.49172.42857115.5714295107.38
7178095411.9116.012.061.02057.0450.99250035.7000000.0335202.067.06171.4166675.0833335344.85
81531160767.900.091.02379.038194.0667.7791214.1444440.24331627.01348.56419.71428626.14285759419.34
9160982005.6387.07.067.0613.0286.51857147.6666670.0243900.00.0087.5714299.5714292005.63
customer_idgross_revenuerecency_dayspurchasesunique_products_purchaseditems_purchasedavg_ticketavg_recency_daysfrequencynumber_returnsmonetary_returnsavg_basket_sizeunique_avg_basketdelta_buy_return
5627177271060.2515.01.066.0645.01060.2500006.01.0000001.017.70645.00000066.01042.55
563717232421.522.02.036.0203.0210.76000012.00.1538460.00.00101.50000018.0421.52
563817468137.0010.02.05.0116.068.5000004.00.4000000.00.0058.0000002.5137.00
564913596697.045.02.0166.0406.0348.5200007.00.2500000.00.00203.00000083.0697.04
5655148931237.859.02.073.0799.0618.9250002.00.6666670.00.00399.50000036.51237.85
565912479473.2011.01.030.0382.0473.2000004.01.0000002.049.90382.00000030.0423.30
568014126706.137.03.015.0508.0235.3766673.00.7500001.062.50169.3333335.0643.63
5686135211092.391.03.0435.0733.0364.1300004.50.3000000.00.00244.333333145.01092.39
569615060301.848.04.0120.0262.075.4600001.02.0000000.00.0065.50000030.0301.84
571512558269.967.01.011.0196.0269.9600006.01.0000001.0269.96196.00000011.00.00